An Educational Innovation Project Focused on the Implementation of Biometrics in Portable Devices

This work describes an educational project that explains how to extract biometric features from biometric samples acquired with microelectronic sensors suitable for portable devices (wearables and mobile phones) by satisfying low cost and reduced size. Specifically, fingerprints, ECG (electrocardiogram) and pulse signals, faces and veins are considered as biometric traits. Acquisition methods associated with this are fingerprint sensors, ECG electrodes and pulse sensors for wearables (in this work, a Raspberry Pi), and cameras for the acquisition of faces and veins in mobile phones. Then, it is explained how features are extracted and compared using biometric recognition algorithms selected to be implemented in portable devices: the feature QFingerMap16 (QFM16) for fingerprints, ECG and pulse waves, FaceNet embeddings for faces, and SIFT (Scale Invariant Feature Transform) for veins. Finally, it is explained how the features extracted are extensively evaluated by using public databases that contain samples acquired with sensors suitable for portable devices. Theoretical and experimental material for this research line and application field are distributed in four sessions lasting 4 hours and 30 minutes.